| dc.contributor.advisor | 蔡政憲 | zh_TW |
| dc.contributor.author (Authors) | 李智慧 | zh_TW |
| dc.contributor.author (Authors) | Lee, Chi-Hui | en_US |
| dc.creator (作者) | 李智慧 | zh_TW |
| dc.creator (作者) | Lee, Chi-Hui | en_US |
| dc.date (日期) | 2022 | en_US |
| dc.date.accessioned | 1-Aug-2022 17:33:09 (UTC+8) | - |
| dc.date.available | 1-Aug-2022 17:33:09 (UTC+8) | - |
| dc.date.issued (上傳時間) | 1-Aug-2022 17:33:09 (UTC+8) | - |
| dc.identifier (Other Identifiers) | G0109358024 | en_US |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/141080 | - |
| dc.description (描述) | 碩士 | zh_TW |
| dc.description (描述) | 國立政治大學 | zh_TW |
| dc.description (描述) | 風險管理與保險學系 | zh_TW |
| dc.description (描述) | 109358024 | zh_TW |
| dc.description.abstract (摘要) | 本研究使用台灣壽險業1993年至2019年個人壽險涵蓋生死合險、終身壽險、定期壽險之解約失效率資料，建構保單年度解約失效率之模型。首先透過主成分分析PCA將資料從15個保單年度降至8個主成分，接著以ARMA模型配適8個主成分之時間序列，並檢驗緊急資金假說、利率假說，最後以VAR模型模擬總體經濟變數，將總體經濟之模擬結果帶入主成分之ARMA模型模擬主成分，將主成分轉換回保單年度解約失效率，完成保單年度結構之解約失效率模型。資料分析顯示解約失效率之保單年度結構大致可分為3個時期，2006年之前保單解約失效率有凸向性，保單年度1最高，保單年度2驟降，之後的保單年度解約失效率持續的遞減；2007至2015年，保單年度解約失效率仍維持遞減之趨勢，但解約失效率最高可能發生在保單年度1、2、4、7；而2016年之後解約失效率隨保單年度遞減趨勢不復存在，而保單解約失效率最高皆發生在保單年度7。 | zh_TW |
| dc.description.abstract (摘要) | In this study, we use Taiwan personal endowment, term-life and whole life insurance lapse data from 1993 to 2019 to construct the Taiwan life insurance lapse model in policy year structure . First step, we use principle component analysis to transform the data from 15 policy years to 8 principle components with 99.5% explanatory power. Second step, we use time series analysis method ARMA model, for each principle components, find the best fit ARMA model. Also test the Emergency Fund Hypothesis and Interest Hypothesis. Third step use vector autoregressive (VAR) model to construct the model of macroeconomic variables, and forecast for 20 years. Forth step, we apply the forecast macroeconomic variables in 8 principle components ARMA model and simulate 10000 times. Final step, we transform the 8 principle component back to 15 policy year, calculate the mean and 95% confidence interval. The result is the policy year structure of lapse rate in Taiwan.From data analysis, we discover that the policy year structure of lapse rate have changed, and can be divide in three periods. Before 2006 the policy year structure of lapse rate is convex. The lapse rate is highest in policy year 1, then drop in policy year 2, and then decay in the following policy years. From 2007 to 2015, the policy year structure of lapse rate has decreasing tendency, but the highest lapse rate can be observed in policy year1,2,4,or 7. After 2016, policy year structure of lapse rate no longer has the decreasing tendency, and the highest lapse rate all be observed in policy year 7. | en_US |
| dc.description.tableofcontents | 第一章、緒論 1第二章、文獻回顧 3第一節、影響解約失效率之假說 3第二節、解約失效行為研究之發展 4第三節、研究方向 7第三章、資料與模型 8第一節、資料內容 8第二節、資料分析 11第二節、研究方法 23第四章、實證模型分析 29第一節、PCA分析 29第二節、ARMA分析 33第三節、外生變數VAR模型 38第四節、模型模擬 40第五章、結論與建議 44第一節、結論 44第二節、未來研究方向 45參考文獻 46附錄 48 | zh_TW |
| dc.format.extent | 6700029 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.source.uri (資料來源) | http://thesis.lib.nccu.edu.tw/record/#G0109358024 | en_US |
| dc.subject (關鍵詞) | 保單年度 | zh_TW |
| dc.subject (關鍵詞) | 解約失效率 | zh_TW |
| dc.subject (關鍵詞) | 主成分分析PCA | zh_TW |
| dc.subject (關鍵詞) | ARMA | zh_TW |
| dc.subject (關鍵詞) | policy year structure | en_US |
| dc.subject (關鍵詞) | lapse rate | en_US |
| dc.subject (關鍵詞) | principle component analysis | en_US |
| dc.subject (關鍵詞) | ARMA model | en_US |
| dc.title (題名) | 台灣壽險業解約率之保單年度結構 | zh_TW |
| dc.title (題名) | The Policy Year Structure of Lapse Rate in Taiwan | en_US |
| dc.type (資料類型) | thesis | en_US |
| dc.relation.reference (參考文獻) | 1.杜於叡, 2014, 建構台灣壽險業解約率期限結構, 國立政治大學風險管理與保險學系研究所.2.林冠勳, 2016, 影響壽險解約行為因素之實證分析, 國立政治大學金融學系研究所.3.徐宇喬, 2015, 動態解約率隊壽險業保費及準備金之影響, 國立政治大學風險管理與保險學系研究所.4.Dodds, A. Dar ; C., 1989, Interest Rates, the Emergency Fund Hypothesis and Saving through Endowment Policies- Some Empirical Evidence for the U.K.5.Eling, Martin, and Dieter Kiesenbauer, 2014, What Policy Features Determine Life Insurance Lapse? An Analysis of the German Market. Journal of Risk and Insurance 81(2):241-269.6.Eling, Martin, and Michael Kochanski, 2013, Research on lapse in life insurance: what has been done and what needs to be done? The Journal of Risk Finance 14(4):392-413.7.Enders, Walter, 2014, Applied Econometric Time Series.8.Hwang, Yawen, Linus Fang-Shu Chan, and Chenghsien Jason Tsai, 2021, On Voluntary Terminations of Life Insurance: Differentiating Surrender Propensity From Lapse Propensity Across Product Types. North American Actuarial Journal 26(2):252-282.9.Jiang, Shi-Jie, 2010, Voluntary Termination of Life Insurance Policies. North American Actuarial Journal 14(4):369-380.10.Kim, Changki, 2005, Modeling Surrender and Lapse Rates With Economic Variables. North American Actuarial Journal 9(4):56-70.11.Kuo, Weiyu, Chenghsien Tsai, and Wei‐Kuang Chen, 2003, An Empirical Study on the Lapse Rate: The Cointegration Approach. Journal of Risk and Insurance 70(3):489-508.12.Outreville, J. F., 1990, Whole-Life Insurance Lapse Rates and the Emergency Fund Hypothesis. Insurance Mathematics & Economics 9(4):249-255.13.Tsai, Chenghsien, Weiyu Kuo, and Derek Mi-Hsiu Chiang, 2009, The Distributions of Policy Reserves Considering the Policy-Year Structures of Surrender Rates and Expense Ratios. Journal of Risk and Insurance 76(4):909-931.14.Tsai, Chenghsien;Weiyu Kuo;Weiyu Kuo, 2002, Early surrender and the distribution of policy reserves.15.楊奕農, 2017, 時間序列分析：經濟與財務上之應用 | zh_TW |
| dc.identifier.doi (DOI) | 10.6814/NCCU202200975 | en_US |
